pages/2_Consult.py

# /home/user/app/pages/2_Consult.py
import streamlit as st
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from datetime import datetime
from typing import List, Optional, Dict, Any
from sqlmodel import select

from config.settings import settings
from agent import get_agent_executor # This now returns the OpenAI-based agent executor
from models import ChatMessage, ChatSession
from models.db import get_session_context
from services.logger import app_logger
from services.metrics import log_consultation_start # Assuming this function exists

# --- Authentication Check ---
if not st.session_state.get("authenticated_user_id"):
    st.warning("Please log in to access the consultation page.")
    try:
        st.switch_page("app.py") # Redirect to the main login page (app.py)
    except st.errors.StreamlitAPIException:
        # This can happen if st.switch_page is called when not in a multipage app context (e.g. dev)
        st.info("Please navigate to the main login page.")
    st.stop() # Halt script execution

authenticated_user_id = st.session_state.get("authenticated_user_id")
authenticated_username = st.session_state.get("authenticated_username", "User") # Default to "User"
app_logger.info(f"User '{authenticated_username}' (ID: {authenticated_user_id}) accessed Consult page.")

# --- Initialize Agent ---
# This will now initialize the OpenAI agent via get_agent_executor() from agent.py
try:
    agent_executor = get_agent_executor()
    app_logger.info("OpenAI-based agent executor initialized successfully for Consult page.")
except ValueError as e: # Catch specific error from get_agent_executor if API key is missing
    st.error(f"AI Agent Initialization Error: {e}")
    app_logger.critical(f"Fatal: AI Agent could not be initialized in Consult page: {e}", exc_info=True)
    st.info("Please ensure the OPENAI_API_KEY is correctly configured in the application settings (Hugging Face Secrets).")
    st.stop()
except Exception as e: # Catch any other unexpected error during agent init
    st.error(f"An unexpected error occurred while initializing the AI Agent: {e}")
    app_logger.critical(f"Fatal: Unexpected AI Agent initialization error: {e}", exc_info=True)
    st.stop()


# --- Session State for Consult Page ---
# Using more descriptive key for the patient context dictionary
if 'current_consult_patient_context_dict' not in st.session_state:
    st.session_state.current_consult_patient_context_dict = {}
if 'consult_context_submitted' not in st.session_state:
    st.session_state.consult_context_submitted = False

# --- Helper Functions ---
@st.cache_data(ttl=30, show_spinner=False, max_entries=10) # Cache agent history for current session
def load_chat_history_for_agent(session_id: int) -> List[Any]: # List of LangChain BaseMessage
    """Loads chat history from DB, formatted for LangChain agent (HumanMessage, AIMessage, SystemMessage)."""
    messages = []
    app_logger.debug(f"Loading agent chat history from DB for session_id: {session_id}")
    try:
        with get_session_context() as db:
            statement = select(ChatMessage).where(ChatMessage.session_id == session_id).order_by(ChatMessage.timestamp)
            db_messages = db.exec(statement).all()
            for msg in db_messages:
                if msg.role == "user": messages.append(HumanMessage(content=msg.content))
                elif msg.role == "assistant": messages.append(AIMessage(content=msg.content))
                elif msg.role == "system": messages.append(SystemMessage(content=msg.content))
                # ToolMessages are usually part of agent_scratchpad, not general chat_history for invoke
        app_logger.debug(f"Loaded {len(messages)} LangChain messages for agent history (session {session_id}).")
    except Exception as e:
        app_logger.error(f"Error loading chat history for session {session_id}: {e}", exc_info=True)
        st.toast(f"Error loading chat history: {e}", icon="⚠️") # Non-blocking error
    return messages

def save_chat_message_to_db(session_id: int, role: str, content: str, tool_call_id: Optional[str]=None, tool_name: Optional[str]=None):
    """Saves a chat message to the database."""
    app_logger.debug(f"Saving message to DB for session {session_id}: Role='{role}', Content snippet='{content[:50]}...'")
    try:
        with get_session_context() as db:
            chat_message_obj = ChatMessage(
                session_id=session_id, role=role, content=content, timestamp=datetime.utcnow(),
                tool_call_id=tool_call_id, tool_name=tool_name
            )
            db.add(chat_message_obj) # Commit will be handled by the context manager
        app_logger.info(f"Message (Role: {role}) saved to DB for session {session_id}.")
    except Exception as e:
        app_logger.error(f"Error saving chat message to DB for session {session_id}: {e}", exc_info=True)
        st.toast(f"Error saving message: {e}", icon="⚠️")


def update_chat_session_with_context_summary_in_db(session_id: int, context_summary: str):
    """Updates the ChatSession record with the patient context summary."""
    try:
        with get_session_context() as db:
            session_to_update = db.get(ChatSession, session_id) # Fetch by primary key
            if session_to_update:
                session_to_update.patient_context_summary = context_summary
                db.add(session_to_update) # SQLModel handles add for updates if PK is present
                app_logger.info(f"Updated ChatSession {session_id} with patient context summary in DB.")
            else:
                app_logger.error(f"Could not find ChatSession {session_id} in DB to update context summary.")
    except Exception as e:
        app_logger.error(f"Error updating chat session {session_id} context summary: {e}", exc_info=True)
        st.toast(f"Error saving context summary: {e}", icon="⚠️")


# --- Page Logic ---
st.title("AI Consultation Room")
st.markdown(f"Interacting as: **{authenticated_username}**")
# Prominent disclaimer on the consult page itself
st.warning(f"**Reminder & Disclaimer:** {settings.MAIN_DISCLAIMER_LONG} {settings.SIMULATION_DISCLAIMER}")


chat_session_id = st.session_state.get("current_chat_session_id")
if not chat_session_id:
    st.error("Error: No active chat session ID found. This is unexpected after login. Please try logging out and logging back in. If the problem persists, please contact support.")
    app_logger.critical(f"User '{authenticated_username}' (ID: {authenticated_user_id}) on Consult page encountered MISSING current_chat_session_id.")
    st.stop()

# --- Patient Context Input Form ---
if not st.session_state.consult_context_submitted:
    st.subheader("Step 1: Provide Patient Context (Optional, Use Simulated Data Only)")
    with st.form(key="patient_context_form_consult_page_openai"): # Unique key
        st.markdown("**Crucial Reminder: Use only anonymized, simulated data for this demonstration. Do NOT enter real PHI.**")
        age_in = st.number_input("Patient Age (Simulated)", min_value=0, max_value=120, step=1, value=None, help="Leave blank if not applicable.")
        gender_options = ["Not Specified", "Male", "Female", "Other"]
        gender_in = st.selectbox("Patient Gender (Simulated)", gender_options, index=0)
        cc_in = st.text_area("Chief Complaint / Reason for Consult (Simulated)", height=100, placeholder="e.g., Persistent cough for 2 weeks, fatigue")
        hist_in = st.text_area("Key Medical History (Simulated)", height=100, placeholder="e.g., Type 2 Diabetes (controlled), Hypertension (on medication), Asthma (mild intermittent)")
        meds_in = st.text_area("Current Medications (Simulated)", height=100, placeholder="e.g., Metformin 500mg BID, Lisinopril 10mg OD, Salbutamol inhaler PRN")
        submit_context_btn = st.form_submit_button("Start Consult with this Context")

        if submit_context_btn:
            raw_context = {
                "Age": age_in, "Gender": gender_in, "Chief Complaint": cc_in,
                "Key Medical History": hist_in, "Current Medications": meds_in,
            }
            # Filter out None/empty/"Not Specified" values for a cleaner context dictionary
            filtered_context_dict = {
                k: v for k, v in raw_context.items()
                if v is not None and str(v).strip() and \
                   (isinstance(v, str) and v.lower() != "not specified") and \
                   (isinstance(v, int) and v > 0 or not isinstance(v, int)) # ensure age > 0 if int
            }
            st.session_state.current_consult_patient_context_dict = filtered_context_dict

            if filtered_context_dict:
                context_summary_str = "; ".join([f"{k}: {v}" for k, v in filtered_context_dict.items()])
            else:
                context_summary_str = "No specific patient context was provided for this session."
            
            update_chat_session_with_context_summary_in_db(chat_session_id, context_summary_str)
            # Save a system message to DB indicating context was set for this session
            save_chat_message_to_db(chat_session_id, "system", f"Initial Patient Context Set: {context_summary_str}")
            
            st.session_state.consult_context_submitted = True
            app_logger.info(f"Patient context submitted for session {chat_session_id}: {context_summary_str}")
            st.rerun() # Rerun to hide form and proceed to chat interface
    st.stop() # Don't proceed to chat interface until context form is handled

# --- Chat Interface (Shown after context is submitted) ---
st.subheader("Step 2: Interact with AI Health Navigator")
agent_history_key = f"agent_chat_history_{chat_session_id}" # Session-specific key for agent's message history

# Initialize or load agent's chat history (list of LangChain messages)
if agent_history_key not in st.session_state:
    st.session_state[agent_history_key] = load_chat_history_for_agent(chat_session_id)
    if not st.session_state[agent_history_key]: # If history is empty after loading from DB
        try:
            log_consultation_start(user_id=authenticated_user_id, session_id=chat_session_id)
        except Exception as e_metric:
            app_logger.warning(f"Failed to log consultation start metric: {e_metric}")
        
        initial_ai_message_content = "Hello! I am your AI Health Navigator. How can I assist you today?"
        patient_context_for_greeting = st.session_state.get('current_consult_patient_context_dict', {})
        if patient_context_for_greeting: # Check if the dict itself is non-empty
            initial_ai_message_content += " I have noted the patient context you provided."
        
        # Add initial AI message to agent's history and save to DB
        st.session_state[agent_history_key].append(AIMessage(content=initial_ai_message_content))
        save_chat_message_to_db(chat_session_id, "assistant", initial_ai_message_content)
        app_logger.info(f"Initialized new consultation (session {chat_session_id}) with a greeting.")

# Display chat messages for UI (fetch fresh from DB for UI consistency)
# This uses a scrollable container.
chat_display_container = st.container(height=450)
with chat_display_container:
    with get_session_context() as db:
        stmt = select(ChatMessage).where(ChatMessage.session_id == chat_session_id).order_by(ChatMessage.timestamp)
        ui_messages_from_db = db.exec(stmt).all()
        for msg in ui_messages_from_db:
            if msg.role == "system": continue # Don't display raw system messages in chat UI
            avatar_icon = "🧑‍⚕️" if msg.role == "assistant" else "👤"
            if msg.role == "tool": avatar_icon = "🛠️" # Example if you log tool messages for UI
            with st.chat_message(msg.role, avatar=avatar_icon):
                st.markdown(msg.content) # Add source/confidence here if msg object from DB supports it

# Chat input from user
if user_prompt := st.chat_input("Ask the AI... (e.g., 'What is hypertension?')"):
    # Display user message in UI immediately
    with chat_display_container: # Also add to the container
        with st.chat_message("user", avatar="👤"):
            st.markdown(user_prompt)
    
    save_chat_message_to_db(chat_session_id, "user", user_prompt)
    st.session_state[agent_history_key].append(HumanMessage(content=user_prompt))

    # Get AI response
    with chat_display_container: # Add AI response to the container
        with st.chat_message("assistant", avatar="🧑‍⚕️"):
            thinking_message = st.empty() # Placeholder for "AI is thinking..."
            thinking_message.markdown("▌") # Simple animated cursor
            
            try:
                # Prepare patient context string for the agent
                patient_context_dict = st.session_state.get('current_consult_patient_context_dict', {})
                if patient_context_dict: # If there's any context
                    context_parts_for_invoke = [f"{k}: {v}" for k, v in patient_context_dict.items()]
                    patient_context_str_for_invoke = "; ".join(context_parts_for_invoke)
                else: # If no context was provided or all fields were empty/default
                    patient_context_str_for_invoke = "No specific patient context was provided by the user for this interaction."

                # These are the keys expected by the OpenAI Functions Agent prompt
                invoke_payload = {
                    "input": user_prompt,
                    "chat_history": st.session_state[agent_history_key], # List of BaseMessage
                    "patient_context": patient_context_str_for_invoke
                }
                app_logger.debug(f"Invoking OpenAI agent with payload: {invoke_payload}")
                
                thinking_message.markdown("AI is thinking...") # Update spinner text

                response = agent_executor.invoke(invoke_payload)
                
                ai_response_content = response.get('output', "I could not generate a valid response at this time.")
                if not isinstance(ai_response_content, str): ai_response_content = str(ai_response_content)
                
                app_logger.info(f"OpenAI Agent response for session {chat_session_id}: '{ai_response_content[:100]}...'")
                thinking_message.empty() # Clear "thinking..." message
                st.markdown(ai_response_content) # Display AI response
                
                save_chat_message_to_db(chat_session_id, "assistant", ai_response_content)
                st.session_state[agent_history_key].append(AIMessage(content=ai_response_content))

            except Exception as e:
                app_logger.error(f"Error during OpenAI agent invocation for session {chat_session_id}: {e}", exc_info=True)
                error_type_name = type(e).__name__
                user_friendly_error = f"Sorry, an error occurred ({error_type_name}). Please try rephrasing your query or contact support if the issue persists."
                thinking_message.empty() # Clear "thinking..." message
                st.error(user_friendly_error) # Display error in the AI's bubble
                
                db_error_msg = f"System encountered an error: {error_type_name}. Details logged."
                save_chat_message_to_db(chat_session_id, "assistant", db_error_msg)
                st.session_state[agent_history_key].append(AIMessage(content=f"Note to self: Encountered error ({error_type_name})."))
    
    st.rerun() # Rerun to ensure the new messages are at the bottom of the scrollable container